提出兼顾高阶邻居与共现关系的图神经网络，提升链接预测准确率。
High-order Graph Neural Networks with Common Neighbor Awareness for Link Prediction
- 引入多跳共现邻居计算相关性得分
- 在消息传递中融合共现信息，增强节点交互建模
- 在3个真实动态图上超越主流模型
链接预测是动态图学习（DGL）中的基础任务，其性能受图拓扑结构深刻影响。近年来，动态图神经网络（DGNN）通过消息传递机制建模节点间关系，显著提升了链接预测性能。然而，现有DGNN高度依赖成对节点交互，忽略了动态图中常见的共现邻居关系。为此，本文提出高阶图神经网络结合共现感知机制（HGNN-CNA），包含两个核心思路：(a) 通过考虑多跳共现邻居来估计相关性得分，捕捉节点间的复杂交互；(b) 将相关性融合进消息传递过程，直接在动态图学习中引入共现邻居交互。在三个真实动态图上的实验表明，所提HGNN-CNA在链接预测任务上显著优于多个先进模型。
原文摘要 · Abstract (English)
Link prediction is a fundamental task in dynamic graph learning (DGL), inherently shaped by the topology of the DG. Recent advancements in dynamic graph neural networks (DGNN), primarily by modeling the relationships among nodes via a message passing scheme, have significantly improved link prediction performance. However, DGNNs heavily rely on the pairwise node interactions, which neglect the common neighbor interaction in DGL. To address this limitation, we propose a High-order Graph Neural Networks with Common Neighbor Awareness (HGNN-CNA) for link prediction with two-fold ideas: a) estimating correlation score by considering multi-hop common neighbors for capturing the complex interaction between nodes; b) fusing the correlation into the message-passing process to consider common neighbor interaction directly in DGL. Experimental results on three real DGs demonstrate that the proposed HGNN-CNA acquires a significant accuracy gain over several state-of-the-art models on the link prediction task.
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